Lottery-Based Evaluations of Early Education Programs: Opportunities and Challenges for Building the Next Generation of Evidence - Christina Weiland; Rebecca Unterman; Susan Dynarski; Rachel Abenavoli; Howard Bloom; Breno Braga; Anne-Marie Faria; Erica Greenberg; Brian A. Jacob; Jane Arnold Lincove; Karen Manship; Meghan McCormick; et al. (AERA Open, 2024)
Lottery-based identification strategies offer potential for generating the next generation of evidence on U.S. early education programs. The authors’ collaborative network of five research teams applying this design in early education settings and methods experts has identified six challenges that need to be carefully considered in this next context: (a) available baseline covariates that may not be very rich; (b) limited data on the counterfactual; (c) limited and inconsistent outcome data; (d) weakened internal validity due to attrition; (e) constrained external validity due to who competes for oversubscribed programs; and (f) difficulties answering site-level questions with child-level randomization. The authors offer potential solutions to these six challenges and concrete recommendations for the design of future lottery-based early education studies.